Segmentation of neurons in microscopy images

version 1.0.3 (1.25 MB) by Omer Yuval
Set up, train and apply a neural network for segmentation of neurons in microscopy images.

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Updated 13 Aug 2021

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Quick start:
0. Set network and training parameters in Params.m.
1. Prepare input and output images:
Im_In = {Im_In_1,Im_In_2,Im_In_3};
Im_Out = {Im_Out_1,Im_Out_2,Im_Out_3};
2. Generate training set:
Generate_Dataset(Im_In,Im_Out);
3. Train:
net = Train;
4. Apply the trained network to an image:
[Im_Out,Im_Label] = Segment_Neuron(net,Im_In_4);
imshow(Im_Label);
* Example raw and annotated neuron images can be found in this paper [1].
* An example pre-trained network is included.
* Please cite this paper [1].
Advanced options:
- Control sample size (see "Input_Size" in Params.m).
- Control class weights during training (see "Class_Weights" in Params.m).
- Control the minimum number of neuron pixels in training samples (see "Functions" block in Params.m).
- You can generate the training set locally and train on another machine (see "Paths" block in Params.m).

Cite As

Omer Yuval (2022). Segmentation of neurons in microscopy images (https://www.mathworks.com/matlabcentral/fileexchange/97547-segmentation-of-neurons-in-microscopy-images), MATLAB Central File Exchange. Retrieved .

Yuval, Omer, et al. “Neuron Tracing and Quantitative Analyses of Dendritic Architecture Reveal Symmetrical Three-Way-Junctions and Phenotypes of Git-1 in C. Elegans.” PLOS Computational Biology, edited by Hugues Berry, vol. 17, no. 7, Public Library of Science (PLoS), July 2021, p. e1009185, doi:10.1371/journal.pcbi.1009185.

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MATLAB Release Compatibility
Created with R2021a
Compatible with R2020b and later releases
Platform Compatibility
Windows macOS Linux

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